Computers are increasingly used in the simulation of natural phenomena such as floods. However, these simulations are based on numerical approximations of equations formalizing our conceptual understanding of flood flows. Thus, model results are intrinsically subject to uncertainty and the use of probabilistic approaches seems more appropriate. Uncertain, probabilistic floodplain maps are widely used in the scientific domain, but still not sufficiently exploited to support the development of flood mitigation strategies.
In this thesis the major sources of uncertainty in flood inundation models are analyzed, resulting in the generation of probabilistic floodplain maps. The utility of probabilistic model output is assessed using value of information and the prospect theory. The use of these maps to support decision making in terms of floodplain development under flood hazard threat is demonstrated.
Micah M. Mukolwe is a trained civil engineer with interests in civil infrastructure design, implementation, project planning and management, and the effect (and mitigation) of natural hazards on floodplain receptors using hydroinformatics tools.